Channel estimation for receivers in a wireless communications network employing Orthogonal Frequency-Division Multiplexing (OFDM network) is accomplished by using training or pilot signals. Using training signals for channel estimation requires transmitting training signals with all subcarriers of an OFDM symbol regularly, which results in a large amount of overhead. Therefore, most high data rate wireless communications networks do not employ this method.
On the other hand, a pilot signal only uses a portion of the subcarriers of an OFDM symbol; consequently, it is preferred to use pilot signals for channel estimation. For example, in a burst mode transmission, pilot signals transmitted on some predetermined subcarriers are inserted into a data stream. These pilot signals facilitate channel tracking and channel estimation for coherent detection.
Pilot signals are distributed evenly or unevenly in the time and frequency domains, i.e. they occupy some subcarriers at some of the times evenly or unevenly. In a wireless communications network equipped with multiple antennas, having an antenna adds another dimension for the distribution of pilot signals. Specifically, the pilot signals occupy some subcarriers at some of the times on some of the antennas. Moreover, conventional channel estimation algorithms based on evenly-distributed pilot signals are less effective than those based on unevenly-distributed pilot signals, such as WiMAX.
OFDM subcarriers are categorized into pilot subcarriers, which can carry pilot signals, and data subcarriers, which can only carry data. The channel response of a pilot subcarrier carrying a pilot signal is estimated using the pilot signal, and it is highly correlated with that of an adjacent data subcarrier carrying data. Therefore, the channel response of a data subcarrier can be estimated based on that of one of its adjacent pilot subcarriers.
Generally, a channel estimation algorithm using pilot signals is implemented by using a two-dimensional interpolation method. Furthermore, a two-dimensional interpolation channel estimation algorithm can be implemented by applying a one-dimensional interpolation channel estimation algorithm iteratively for a variable number of times. The one-dimensional interpolation channel estimation algorithm is based on a function of one variable, which is frequency, time, or a third variable comprised of frequency and time. However, for a mobile wireless communications network that has limited unevenly-distributed pilot signals, employing the above one-dimensional algorithm iteratively does not produce an accurate estimation of the channel response of a data subcarrier.
Conventional channel estimation algorithms may incorporate some type of decision feedback mechanism to improve the accuracy of the channel estimation. However, decision feedback mechanisms are based on training signals or evenly distributed pilot signals, and they are not applicable to wireless communication systems such as WiMAX.
As such, what is desired is a method for channel estimation based on unevenly-distributed pilot signals using generalized two-dimensional channel estimation algorithms and a decision feedback mechanism.